Artificial neural network based decentralized current-sharing control for parallel connected DC-DC converters in DC microgrid application

Abstract

To create a non-interrupted, economical and reliable direct current (DC) microgrid, multiple DC-DC converters can be connected in parallel. Current sharing among these multiple converters becomes essential for proper operation of the system. This study proposes an artificial neural network (ANN) based control technique for parallel connected DC-DC boost converters which ensures accurate current sharing according to the specified maximum limits. Levenberg-Marquardt algorithm-based ANN network is used to reduce the training time and, also to achieve near ≈ 100 % accuracy of the training data. ANN control provides better voltage regulation, accurate current sharing unlike the conventional proportional-integral (PI) based control which faces issues such as inaccurate current sharing, high transient, peak overshoots and steady state error during sudden change in the system. The efficient functioning of the proposed control method is verified by simulating two parallel connected DC-DC converters using MATLAB/Simulink. A hardware prototype of converters rating ≈ 250 W using TMS320F28379D Digital signal processor controller is also developed to verify the performance and effectiveness of the ANN based control in comparison to PI based technique. The ANN based technique is faster to achieve the reference voltage, and the peak overshoot is approximately 75 % lesser than the PI based control.